"... Abstract—With the spread of data mining technologies and the accumulation of social data, such technologies and data are being used for determinations that seriously affect people’s lives. For example, credit scoring is frequently determined based on the records of past credit data together with sta ..."

-preserving viewpoint, this can be interpreted as hiding sensitive information when classification results are observed. In this paper, we first discuss three causes of unfairness in machine learning. We then propose a regularizationapproach that is applicable to any prediction algorithm with probabilistic

"... Abstract—Due to the spread of data mining technologies, such technologies are being used for determinations that seriously affect individuals ’ lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needle ..."

. Needless to say, such determinations must be nondiscriminatory and fair in sensitive features, such as race, gender, religion, and so on. The goal of fairness-aware classifiers is to classify data while taking into account the potential issues of fairness, discrimination, neutrality, and/or independence

"... …Truth [is] being involved in an eternal conversation about things that matter, conducted with passion and discipline…truth is not in the conclusions so much as in the process of conversation itself…if you want to be in truth you must be in conversation. Parker Palmer ..."

…Truth [is] being involved in an eternal conversation about things that matter, conducted with passion and discipline…truth is not in the conclusions so much as in the process of conversation itself…if you want to be in truth you must be in conversation. Parker Palmer

"... Up to date, more than 15 billion US dollars have been in-vested in microfinance that benefited more than 160 million people in developing countries. The Kiva organization is one of the successful examples that use a decentralized matching process to match lenders and borrowers. Interested lenders fr ..."

in accordance to their preferences, but should also help to diversify the distribution of donations to reduce the inequality of loans is highly demanded, as altruism, like any resource, can be congestible. In this paper, we propose a fairness-aware recommenda-tion system based on one-class collaborative

"... We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised framework that incorporates labeled and unlabeled data in a general-purpose learner. Some transductive graph learning al ..."

We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised framework that incorporates labeled and unlabeled data in a general-purpose learner. Some transductive graph learning

"... We develop a connectionist approach to processing in quasi-regular domains, as exemplified by English word reading. A consideration of the shortcomings of a previous implementation (Seidenberg & McClelland, 1989, Psych. Rev.) in reading nonwords leads to the development of orthographic and phono ..."

We develop a connectionist approach to processing in quasi-regular domains, as exemplified by English word reading. A consideration of the shortcomings of a previous implementation (Seidenberg & McClelland, 1989, Psych. Rev.) in reading nonwords leads to the development of orthographic

"... The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this p ..."

to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual

"... A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with &quot;flat &quot; data representations. Thus, to apply these methods, we are forced to convert our data into a flat form, thereby losing much ..."

A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with &quot;flat &quot; data representations. Thus, to apply these methods, we are forced to convert our data into a flat form, thereby losing much

"... Abstract. We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over functions. We present the simple equations for incorporating training data and examine how to learn the hyperpar ..."

Abstract. We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over functions. We present the simple equations for incorporating training data and examine how to learn